ReviewCurrent opinion in neurobiology2016
Itinerancy between attractor states in neural systems.
Review in Current opinion in neurobiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
30 citing papers in PubMed.
- Sharp and smooth state transitions during resting state brain dynamics.bioRxiv : the preprint server for biology · 2026Article
- Inhibitory-stabilization is sufficient for history-dependent computation in a randomly connected attractor network.Journal of computational neuroscience · 2026Article
- Convergent information flows explain recurring firing patterns in cerebral cortex.Nature neuroscience · 2026Article
- Article
- EMI-EDU: reframing learning and education through energy, mass and information.Frontiers in human neuroscience · 2026Article
- Asymmetric developmental bifurcations in polarized environments: a new class of human variants, which may include autism.Molecular psychiatry · 2025Review
- Ephaptic coupling and power fluctuations in depression.bioRxiv : the preprint server for biology · 2025Article
- Inhibitory-stabilization is sufficient for history-dependent computation in a randomly connected attractor network.bioRxiv : the preprint server for biology · 2025Article
- Artificial Intelligence-Assisted Speech Therapy for /ɹ/: A Single-Case Experimental Study.American journal of speech-language pathology · 2024Article
- Assessment of Fractal Synchronization during an Epileptic Seizure.Entropy (Basel, Switzerland) · 2024Article
- Reservoir-computing based associative memory and itinerancy for complex dynamical attractors.Nature communications · 2024Article
- Neurodynamical Computing at the Information Boundaries of Intelligent Systems.Cognitive computation · 2024Article
- Multistability in neural systems with random cross-connections.Biological cybernetics · 2023Article
- Accumulating evidence for myriad alternatives: Modeling the generation of free association.Psychological review · 2023Article
- Metastable dynamics of neural circuits and networks.Applied physics reviews · 2022Review
- A series of unforced events.Neuron · 2022Article
- The Mean Field Approach for Populations of Spiking Neurons.Advances in experimental medicine and biology · 2022Article
- The population doctrine in cognitive neuroscience.Neuron · 2021Review
- A model of naturalistic decision making in preference tests.PLoS computational biology · 2021Article
- Binocular rivalry reveals an out-of-equilibrium neural dynamics suited for decision-making.eLife · 2021Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
Converging evidence from neural, perceptual and simulated data suggests that discrete attractor states form within neural circuits through learning and development. External stimuli may bias neural activity to one attractor state or cause activity to transition between several discrete states. Evidence for such transitions, whose timing can vary across trials, is best accrued through analyses that avoid any trial-averaging of data. One such method, hidden Markov modeling, has been effective in this context, revealing state transitions in many neural circuits during many tasks. Concurrently, modeling efforts have revealed computational benefits of stimulus processing via transitions between attractor states. This review describes the current state of the field, with comments on how its perceived limitations have been addressed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.